by Jiang Jun (江骏, ohmystack) Machine Learning GDE (Google Developer Expert) Recap the 2nd TensorFlow Summit for Shanghai TensorFlow fans. I introduced 19 topics in summit, put them into one topic.

1. 江骏 ohmystack
Recap

2. • Machine Learning GDE (Google Developer Expert)
• Focus on Applied Machine Learning and Deep Learning Platform.
江骏 ohmystack

4. Applications

7. Amazing Two Years
https://tensorflow.google.cn/
http://medium.com/tensorflow
http://twitter.com/TensorFlow
https://www.tensorflow.org/
https://www.youtube.com/tensorflow
https://developers.google.com/machine-learning/crash-course/

10. TPU Pods
64 TPUs in a Pod

11. tf.*
tf.keras
Estimator
Flexible low-level APIs
Model building using
high-level primitives
Distributed Execution,
Intergrates TensorBoards,
tensorflow serving, …
Premade EstimatorsModel in a box

13. autoML
Controller proposes machine learning models.
Use the feedback signals as reinforcement learning signals.
Repeat this a lot of times.

14. https://cloud.google.com/automl/

19. Performance
• CNN benchmarks reach > 13,000 images/second with tf.data
• tf.contrib.data.prefetch_to_device() for GPUs in 1.8
(It ensures that the next batch of data in memory of GPU when the next training step begins)

20. Flexibility
• tf.SparseTensor
• Custom Python code via Dataset.from_generator()
• Custom C++ code via DatasetOpKernel plugins

21. • Use Python for loops in eager execution mode
• Standard recipes for tf.train.Example and CSV in 1.8
(handles types, column names for you)
• Integration with Estimators (and Keras)
Easy of use

22. For more information see:
https://www.tensorflow.org/programmers_guide/datasets
https://www.tensorflow.org/performance/datasets_performance
For example code see:
https://github.com/tensorflow/benchmarks
https://github.com/tensorflow/models

26. debug
profiling
with your familiar tools
&

27. Pythonic and object-oriented

28. NOTE: Try to execute enough computation to keep GPU busy,
you’re unlikely to see anything bad from using eager execution.

29. Interoperating with graphs

30. https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples

31. https://www.tensorflow.org/programmers_guide/eager
Give it a try!

33. train / inference
https://developers.google.com/machine-learning/glossary/
训练 （推理理）预测

37. Ops API/Eager

38. Layers API

42. Server side is coming soon.https://js.tensorflow.org/

47. https://github.com/tf-coreml/tf-coreml

53. We cannot do this with Python,
because it doesn’t support the kind of compiler analysis we need.

54. First-class machine learning language and compiler features
https://www.tensorflow.org/community/swift

55. interpretive Swift Notebook
Use with Python APIs
without wrappers

57. Build, share and use modules

58. module instead of model

59. https://www.tensorflow.org/hub/

60. can be pre-trained and fine-tuned

69. https://github.com/tensorflow/transform

72. https://github.com/tensorflow/model-analysis

74. gRPC RESTful

76. http://www.kdd.org/kdd2017/papers/view/tfx-a-tensorflow-based-production-scale-machine-learning-platform
Paper
End-to-End Example
https://github.com/tensorflow/model-analysis/tree/master/examples/chicago_taxi

77. For Developers

78. Applications

79. https://github.com/ohmystack
http://ohmystack.com/
I’m 江骏 / ohmystack
https://www.linkedin.com/in/ohmystack/
https://www.slideshare.net/jiangjun1990/presentations